Genetic programming for domain adaptation in product reviews
Conference Publication ResearchOnline@JCUAbstract
There is a large variety of products sold online and the websites are in several languages. Hence, it is desirable to train a model that can predict sentiments in different domains simultaneously. Previous authors have used deep learning to extract features from multiple domains. Here, each word is represented by a vector that is determined using co-occurrence data. Such a model requires that all sentences have the same length resulting in low accuracy. To overcome this challenge, we model the features in each sentence using a variable length tree called a Genetic Program. The polarity of clauses can be represented using mathematical operators such as '+' or '-' at internal nodes in the tree. The proposed model is evaluated on Amazon product reviews for different products and in different languages. We are able to outperform the accuracy of baseline multi-domain models in the range of 5-20%.
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Publication Name
CEC 2020: IEEE Congress on Evolutionary Computation
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ISBN/ISSN
978-1-7281-6929-3
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Pages Count
8
Location
Glasgow, UK
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/CEC48606.2020.9185713